SOTAVerified

Optical Flow Estimation

Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.

Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.

Further readings:

Definition source: Devon: Deformable Volume Network for Learning Optical Flow

Image credit: Optical Flow Estimation

Papers

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Showing 1651–1700 of 2184 papers

TitleStatusHype
NexusGS: Sparse View Synthesis with Epipolar Depth Priors in 3D Gaussian Splatting—0
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM—0
Non-Contact Breathing Rate Detection Using Optical Flow—0
Nonlinear Evolutionary PDE-Based Refinement of Optical Flow—0
Nonlinear Motion-Guided and Spatio-Temporal Aware Network for Unsupervised Event-Based Optical Flow—0
Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects—0
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics—0
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation—0
Novel Video Prediction for Large-scale Scene using Optical Flow—0
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting—0
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow—0
Object-Centric Diffusion for Efficient Video Editing—0
Object Class Aware Video Anomaly Detection through Image Translation—0
Object Detection in Video with Spatiotemporal Sampling Networks—0
Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation—0
Object Segmentation Tracking from Generic Video Cues—0
Objects that Sound—0
Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker—0
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation—0
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning—0
Occlusion-Aware 3D Motion Interpretation for Abnormal Behavior Detection—0
Occlusion-Aware Networks for 3D Human Pose Estimation in Video—0
Occlusion Aware Unsupervised Learning of Optical Flow—0
Occlusion Detection and Motion Estimation with Convex Optimization—0
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality—0
OFF-ApexNet on Micro-expression Recognition System—0
Offline and Online Optical Flow Enhancement for Deep Video Compression—0
OmniFlow: Human Omnidirectional Optical Flow—0
OmniTrack: Real-time detection and tracking of objects, text and logos in video—0
On an Edge-Preserving Variational Model for Optical Flow Estimation—0
On a spatial-temporal decomposition of the optical flow—0
One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2—0
One-Shot-Learning Gesture Recognition using HOG-HOF Features—0
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network—0
On Flow Profile Image for Video Representation—0
On guiding video object segmentation—0
Online Video Object Segmentation via Convolutional Trident Network—0
On Optical Flow Models for Variational Motion Estimation—0
On-Orbit Smart Camera System to Observe Illuminated and Unilluminated Space Objects—0
On the Importance of Video Action Recognition for Visual Lipreading—0
On the Integration of Optical Flow and Action Recognition—0
On the Space-Time Statistics of Motion Pictures—0
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos—0
Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features—0
OpFlowTalker: Realistic and Natural Talking Face Generation via Optical Flow Guidance—0
OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare—0
Optical Flow and Mode Selection for Learning-based Video Coding—0
Optical flow and scene flow estimation: A survey—0
Optical Flow augmented Semantic Segmentation networks for Automated Driving—0
Optical Flow-based 3D Human Motion Estimation from Monocular Video—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64—Unverified
2FastFlowNet-ftAverage End-Point Error4.89—Unverified
3UnrolledCostAverage End-Point Error4.69—Unverified
4LiteFlowNet-ftAverage End-Point Error4.54—Unverified
5FlowNet2Average End-Point Error3.96—Unverified
6IRR-PWCAverage End-Point Error3.84—Unverified
7SelFlowAverage End-Point Error3.74—Unverified
8FDFlowNet-ftAverage End-Point Error3.71—Unverified
9ScopeFlowAverage End-Point Error3.59—Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36—Unverified
2FastFlowNet-ftAverage End-Point Error6.08—Unverified
3UnrolledCostAverage End-Point Error5.8—Unverified
4MR-FlowAverage End-Point Error5.38—Unverified
5LiteFlowNet-ftAverage End-Point Error5.38—Unverified
6FDFlowNet-ftAverage End-Point Error5.11—Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69—Unverified
8IRR-PWCAverage End-Point Error4.58—Unverified
9LiteFlowNet3-SAverage End-Point Error4.53—Unverified
10ContinualFlow + ftAverage End-Point Error4.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7—Unverified
2FastFlowNetF1-all33.1—Unverified
3FlowNet2F1-all30—Unverified
4VCNF1-all25.1—Unverified
5HD3F1-all24—Unverified
6MaskFlowNetF1-all23.1—Unverified
7SCVF1-all19.3—Unverified
8RAPIDFlowF1-all17.7—Unverified
9CRAFTF1-all17.5—Unverified
10RAFTF1-all17.4—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22—Unverified
2UnrolledCostFl-all10.81—Unverified
3LiteFlowNet-ftFl-all9.38—Unverified
4SelFlowFl-all8.42—Unverified
5IRR-PWCFl-all7.65—Unverified
6LiteFlowNet2-ftFl-all7.62—Unverified
7LiteFlowNet3Fl-all7.34—Unverified
8LiteFlowNet3-SFl-all7.22—Unverified
9MaskFlownet-SFl-all6.81—Unverified
10RAPIDFlowFl-all6.12—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8—Unverified
2LiteFlowNet-ftAverage End-Point Error1.6—Unverified
3IRR-PWCAverage End-Point Error1.6—Unverified
4SelFlowAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3-SAverage End-Point Error1.3—Unverified
9LiteFlowNet3Average End-Point Error1.3—Unverified
10MaskFlownetAverage End-Point Error1.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27—Unverified
2SPyNet1px total29.96—Unverified
3GMFlow1px total10.36—Unverified
4GMA1px total7.07—Unverified
5RAFT1px total6.79—Unverified
6FlowNet21px total6.71—Unverified
7FlowFormer1px total6.51—Unverified
8MS-RAFT+1px total5.72—Unverified
9RPKNet1px total4.81—Unverified
10DPFlow1px total3.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21—Unverified
2MDFlow-FastAverage End-Point Error4.73—Unverified
3UpFlowAverage End-Point Error4.68—Unverified
4ARFlow-MVAverage End-Point Error4.49—Unverified
5MDFlowAverage End-Point Error4.16—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5—Unverified
2MDFlow-FastAverage End-Point Error5.99—Unverified
3ARFlow-MVAverage End-Point Error5.67—Unverified
4MDFlowAverage End-Point Error5.46—Unverified
5UpFlowAverage End-Point Error5.32—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79—Unverified
2MDFlow-FastFl-all11.43—Unverified
3UpFlowFl-all9.38—Unverified
4MDFlowFl-all8.91—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5—Unverified
2UpFlowAverage End-Point Error1.4—Unverified